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exp2/modified/train.py

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+from pathlib import Path
+import sys
+import numpy
+import torch
+from torch.utils.data import DataLoader, Dataset
+import matplotlib.pyplot as plt
+import torch.nn.functional as F
+
+sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
+import gpu_utils
+
+
+class CNN(torch.nn.Module):
+    """卷积神经网络模型"""
+
+    def __init__(self):
+        super(CNN, self).__init__()
+
+        # 使用Ceil模式设置MaxPooling，因为tensorflow默认是这个模式。
+        self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=(3, 3))
+        self.pool1 = torch.nn.MaxPool2d(kernel_size=(2, 2), ceil_mode=True)
+        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=(3, 3))
+        self.pool2 = torch.nn.MaxPool2d(kernel_size=(2, 2), ceil_mode=True)
+        self.conv3 = torch.nn.Conv2d(64, 64, kernel_size=(3, 3))
+        self.flatten = torch.nn.Flatten()
+        # 28x28过第一轮卷积后变为26x26，过第一轮池化后变为13x13
+        # 过第二轮卷积后变为11x11，过第二轮池化后变为5x5
+        # 过第三轮卷积后变为3x3。
+        # 最后一轮卷积核个数为64。
+        self.fc1 = torch.nn.Linear(64 * 3 * 3, 64)
+        self.fc2 = torch.nn.Linear(64, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = self.pool1(x)
+        x = F.relu(self.conv2(x))
+        x = self.pool2(x)
+        x = F.relu(self.conv3(x))
+        x = self.flatten(x)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.softmax(x, dim=1)
+
+
+class MnistDataset(Dataset):
+    """用于加载Mnist的自定义数据集"""
+
+    shape: int
+    x_data: torch.Tensor
+    y_data: torch.Tensor
+
+    def __init__(self, x_data: torch.Tensor, y_data: torch.Tensor):
+        x_len = x_data.shape[0]
+        y_len = y_data.shape[0]
+        assert (x_len == y_len)
+        self.shape = x_len
+
+        self.x_data = x_data
+        self.y_data = y_data
+
+    def __getitem__(self, index):
+        return self.x_data[index], self.y_data[index]
+
+    def __len__(self):
+        return self.shape
+
+
+class DataSource:
+    """用于读取MNIST数据的数据读取器"""
+
+    train_data: DataLoader
+    test_data: DataLoader
+
+    def __init__(self, batch_size: int):
+        datasets_path = Path(
+            __file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
+        datasets = numpy.load(datasets_path)
+
+        # 6万张训练图片：60000x28x28。标签只有第一维。
+        train_images = torch.from_numpy(datasets['x_train'])
+        train_label = torch.from_numpy(datasets['y_train'])
+        # 1万张测试图片：10000x28x28。标签只有第一维。
+        test_images = torch.from_numpy(datasets['x_test'])
+        test_label = torch.from_numpy(datasets['y_test'])
+
+        # 为了符合后面图像的输入颜色通道条件，要在最后挤出一个新的维度
+        train_images.unsqueeze(-1)
+        test_images.unsqueeze(-1)
+        # 像素值归一化
+        train_images /= 255.0
+        test_images /= 255.0
+
+        # 创建数据集
+        train_dataset = MnistDataset(train_images, train_label)
+        test_dataset = MnistDataset(test_images, test_label)
+
+        # 赋值到自身
+        self.train_data = DataLoader(dataset=train_dataset,
+                                     batch_size=batch_size,
+                                     shuffle=True)
+        self.test_data = DataLoader(dataset=test_dataset,
+                                    batch_size=batch_size,
+                                    shuffle=False)
+
+
+def main():
+    n_epoch = 5
+    n_batch_size = 25
+
+    device = gpu_utils.get_gpu_device()
+    data_source = DataSource(n_batch_size)
+    cnn = CNN().to(device)
+
+    optimizer = torch.optim.Adam(cnn.parameters())
+    loss_func = torch.nn.CrossEntropyLoss()
+
+    for epoch in range(n_epoch):
+        cnn.train()
+
+        batch_images: torch.Tensor
+        batch_labels: torch.Tensor
+        for batch_index, (batch_images, batch_labels) in enumerate(data_source.train_data):
+            gpu_images = batch_images.to(device)
+            gpu_labels = batch_labels.to(device)
+
+            optimizer.zero_grad()
+            prediction: torch.Tensor = cnn(gpu_images)
+            loss: torch.Tensor = loss_func(prediction, gpu_labels)
+            loss.backward()
+            optimizer.step()
+
+            loss_showcase = loss.item()
+            print(f'Epoch: {epoch+1}, Batch: {batch_index}, Loss: {loss.item():.4f}')
+
+
+
+if __name__ == "__main__":
+    gpu_utils.print_gpu_availability()
+    main()